Automated Identification of Malaria-Infected Cells and Classification of Human Malaria Parasites Using a Two-Stage Deep Learning Technique - Dhevisha, Sukumarran; Ee, Sam Loh; Anis Salwa, Mohd Khairuddin; Romano, Ngui; Wan Yusoff, Wan Sulaiman; Indra, Vythilingam; Paul Cliff Simon, Divis; Khairunnisa, Hasikin (IEEE, 2024)
The gold standard for diagnosing malaria remains microscopic examination; however, its application is frequently impeded by the lack of a standardized framework that guarantees uniformity and quality, particularly in scenarios with limited resources and high volume. This study suggests a novel and highly effective automated diagnostic approach that employs deep-learning object detectors to improve the accuracy and efficiency of malaria-infected cell detection and Plasmodium species classification to overcome these challenges. Plasmodium parasites were detected within thin blood stain images using the YOLOv4 and YOLOv5 models, which were optimized for this purpose. YOLOv5 obtains a slightly higher accuracy on the source dataset (mAP@ 0.5=96 %) than YOLOv4 (mAP@ 0.5=89 %), but YOLOv4 exhibits superior robustness and generalization across diverse datasets, as demonstrated by its performance on an independent validation set (mAP@ 0.5=90 %). This robustness emphasizes the dependability of YOLOv4 for deployment in a variety of clinical settings. Furthermore, an automated process was implemented to produce bound single-cell images from YOLOv4’s localization outputs, thereby eradicating the necessity for conventional and time-consuming segmentation methods. The DenseNet-121 model, which was optimized for species identification, obtained an impressive overall accuracy of 95.5% in the subsequent classification stage, indicating excellent generalization across all malaria species. Accurate classification of Plasmodium species on microscopically thin blood films is essential for guiding appropriate therapy and preventing unnecessary anti-malarial treatments, which can lead to adverse effects and contribute to drug resistance. This research contributes to the field of automated malaria diagnosis by offering a comprehensive framework that substantially improves clinical decision-making, particularly in resource-limited environments.